Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/130059 
Year of Publication: 
2015
Series/Report no.: 
cemmap working paper No. CWP54/15
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. As a special case, our method yields marginal confidence sets for individual coordinates of this parameter vector. Our inference method controls asymptotic size uniformly over a large class of data distributions. The current literature describes only two other procedures that deliver uniform size control for this type of problem: projection-based and subsampling inference. Relative to projection-based procedures, our method presents three advantages: (i) it weakly dominates in terms of finite sample power, (ii) it strictly dominates in terms of asymptotic power, and (iii) it is typically less computationally demanding. Relative to subsampling, our method presents two advantages: (i) it strictly dominates in terms of asymptotic power (for reasonable choices of subsample size), and (ii) it appears to be less sensitive to the choice of its tuning parameter than subsampling is to the choice of subsample size.
Subjects: 
Partial Identification
Moment Inequalities
Subvector Inference
Hypothesis Testing
JEL: 
C01
C12
C15
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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